Chapter 8: Estimating with Confidence
Section 8.1
Confidence Intervals: The Basics
If you had to give one number to estimate an unknown population
parameter, what would it be? If you were estimating a population
mean µ, you would probably use x. If you were estimating a
population proportion p, you might use p̂ . In both cases, you would
be providing a point estimate of the parameter of interest.
A point estimator is a statistic that provides an estimate of a
population parameter. The value of that statistic from a sample is
called a point estimate. Ideally, a point estimate is our “best guess”
at the value of an unknown parameter.
Example 1: In each of the following settings, determine the point estimator
you would use and calculate the value of the point estimate.
a) Quality control inspectors want to estimate the mean lifetime μ of the AA
batteries produced in an hour at a factory. They select a random sample of
50 batteries during each hour of production and then drain them under
conditions that mimic normal use. Here are the lifetimes (in hours) of the
batteries from one such sample:
Use the sample mean x as a point estimator for the population mean μ. For
these data, our point estimate is x = 16.718 hours.
b) What proportion p of U.S. high school students smoke? The 2011 Youth
Risk Behavioral Survey questioned a random sample of 15,425 students in
grades 9 to 12. Of these, 2792 said they had smoked cigarettes at least one
day in the past month.
Use the sample proportion p̂ as a point estimator for the population
proportion p. For this survey, our point estimate is
pˆ 
2792
 0.181
15, 425
c) The quality control inspectors in part (a) want to investigate the variability
in battery lifetimes by estimating the population variance σ2.
Use the sample variance sx2 as a point estimator for the population
variance σ2. For the battery life data, our point estimate is s x2 = 0.441 hours2.
Activity
The Idea of a Confidence Interval
Recall the “Mystery Mean” Activity. Is the value of the
population mean µ exactly 240.80? Probably not.
However, since the sample mean is 240.80, we could guess
that µ is “somewhere” around 240.80. How close to 240.80 is
µ likely to be?
To answer this question, we must ask another:
How would the sample mean x vary if we took many SRSs
of size 16 from the population?
Shape : Because the population distribution is Normal, so is the sampling distribution of x .
Thus, the Normal / Large Sample condition is met.
Center : The mean of the sampling distribution of x is the same as the unknown
mean of the entire population. That is x  .

20
 5,
n
16
because the 10% condition is met - we are sampling from an infinite population in this case.
Spread : The standard deviation of x for an SRS of 16 observations is  x 

Example 2: When Mr. Schiel’s class discussed the results of the mystery mean Activity,
students used the following logic to come up with an “interval estimate” for the unknown
population mean μ.
1. The sample mean x = 240.80 is our point estimate for  . We don’t expect x to be exactly
equal to  , so we want to say how precise this estimate is.
2. In repeated samples, the values of x follow a Normal
distribution with mean  and standard deviation 5,
as in the figure to the right.
3. The 95 part of the 68-95-99.7 rule for Normal
distributions says that x is within 2  5   10
 that’s 2 standard deviations  of the population
mean  in about 95% of all samples of size
n  16. See the figure to the right.
4. Whenever x is within 10 points of  ,  is within 10 points of x . This happens in about 95% of
all possible samples. So the interval from x  10 to x + 10 “captures” the population mean  in
about 95% of all samples of size 16.
5. If we estimate that  lies somewhere in the interval from
x  10  240.80 – 10  230.80 to x  10  240.80  10  250.80
we’d be calculating an interval using a method that captures the true  in about 95% of all
possible samples of this size.
A C% confidence interval gives an interval of plausible values for a parameter. The
interval is calculated from the data and has the form
point estimate ± margin of error
The difference between the point estimate and the true parameter value will be less than
the margin of error in C% of all samples. The margin of error tells how close the
estimate tends to be to the unknown parameter in repeated random sampling.
The confidence level C gives the overall success rate of the method for calculating the
confidence interval. That is, in C% of all possible samples, the method would yield an
interval that captures the true parameter value.
Interpreting Confidence Intervals and Confidence Levels
Confidence interval: To interpret a C% confidence interval for an unknown parameter,
say, “We are C% confident that the interval from_____ to _____ captures the [parameter
in context].”
Example 3: Two weeks before a presidential election, a polling organization asked a random
sample of registered voters the following question: “If the presidential election were held
today, would you vote for candidate A or candidate B?” Based on this poll, the 95%
confidence interval for the population proportion who favor candidate A is (0.48, 0.54).
a) Interpret the confidence interval.
We are 95% confident that the interval from 0.48 to 0.54 captures the true proportion of all
registered voters who favor candidate A in the election.
b) What is the point estimate that was used to create the interval? What is the margin of error?
A confidence interval has the form
point estimate ± margin of error
The point estimate is at the midpoint of the interval. Here, the point estimate is p̂ = 0.51. The
margin of error gives the distance from the point estimate to either end of the interval. So the
margin of error for this interval is 0.03.
c) Based on this poll, a political reporter claims that the majority of registered voters
favor candidate A. Use the confidence interval to evaluate this claim.
Any value from 0.48 to 0.54 is a plausible value for the population proportion p that
favors candidate A. Because there are plausible values of p less than 0.50, the
confidence interval does not give convincing evidence to support the reporter’s
claim that a majority (more than 50%) of registered voters favor candidate A.
Activity
Confidence level: To say that we are 95% confident is shorthand for “If we take
many samples of the same size from this population, about 95% of them will result
in an interval that captures the actual parameter value.”
The confidence level tells us how likely it is that the method we are using will
produce an interval that captures the population parameter if we use it many times.
However, in practice we tend to calculate only a single confidence interval for a
given situation.
The confidence level does not tell us the chance that a particular confidence
interval captures the population parameter.
Instead, the confidence interval gives us a set of plausible values for the parameter.
Example 4: Interpreting a confidence level.
The confidence level in the mystery mean example—
roughly 95%—tells us that if we take many SRSs of
size 16 from Mr. Schiel’s mystery population, the
Interval x ± 10 will capture the population mean μ for
about 95% of those samples.
Be sure you understand the basis for our confidence. There are only two
possibilities:
1. The interval from 230.80 to 250.80 contains the population mean μ.
2. The interval from 230.80 to 250.80 does not contain the population mean μ. Our
SRS was one of the few samples for which x is not within 10 points of the
true μ. Only about 5% of all samples result in a confidence interval that fails to
capture μ.
What’s the probability that our 95% confidence interval captures the
parameter?
It’s not 95%! Before we execute the command mean(randNorm(M,20,16)), we have
a 95% chance of getting a sample mean that’s within 2 x of the mystery μ, which
would lead to a confidence interval that captures μ. Once we have chosen a random
sample, the sample mean x either is or isn’t within 2 x of μ. And the resulting
confidence interval either does or doesn’t contain μ. After we construct a confidence
interval, the probability that it captures the population parameter is either 1 (it
does) or 0 (it doesn’t).
Example 5: The Pew Internet and American Life Project asked a random sample of
2253 U.S. adults, “Do you ever…use Twitter or another service to share updates
about yourself or to see updates about others?” Of the sample, 19% said “Yes.”
According to Pew, the resulting 95% confidence interval is (0.167, 0.213). Interpret
the confidence interval and the confidence level.
Confidence interval: We are 95% confident that the interval from 0.167 to 0.213
captures the true proportion p of all U.S. adults who use Twitter or another service
for updates.
Confidence level: If many samples of 2253 U.S. adults were taken, the resulting
confidence intervals would capture the true proportion of all U.S. adults who use
Twitter or another service for updates for about 95% of those samples.
Constructing a Confidence Interval
Why settle for 95% confidence when estimating a parameter? The price we pay for greater
confidence is a wider interval.
When we calculated a 95% confidence interval for the mystery mean µ, we started with
estimate ± margin of error
Our estimate came from the sample statistic x .
Since the sampling distribution of x is Normal,
about 95% of the values of x will lie within 2
standard deviations (2 x ) of the mystery mean  .
That is, our interval could be written as:
240.80  2  5  x  2 x
This leads to a more general formula for confidence intervals:
statistic ± (critical value) • (standard deviation of statistic)
The critical value is a multiplier that makes the interval wide enough to have the stated
capture rate. The critical value depends on both the confidence level C and the sampling
distribution of the statistic.
Calculating a Confidence Interval
The confidence interval for estimating a population parameter has the form
statistic ± (critical value) • (standard deviation of statistic)
where the statistic we use is the point estimator for the parameter.
Properties of Confidence Intervals:
*The user chooses the confidence level, and the margin of error follows from this choice.
*We would like high confidence and also a small margin of error. High confidence says that
our method almost always gives correct answers. A small margin of error says that we have
pinned down the parameter quite precisely.
*Our general formula for a confidence interval is
Properties of Confidence Intervals Cont.:
*We can see that the margin of error depends on the critical value and the standard
deviation of the statistic. The critical value is tied directly to the confidence level: greater
confidence requires a larger critical value.
*The standard deviation of the statistic depends on the sample size n: larger samples give
more precise estimates, which means less variability in the statistic.
*The margin of error gets smaller when:
 The confidence level decreases. There is a trade-off between the confidence level and
the margin of error. To obtain a smaller margin of error from the same data, you must
be willing to accept lower confidence.
 The sample size n increases. Increasing the sample size n reduces the margin of error
for any fixed confidence level.
Using Confidence Intervals Wisely
Here are two important cautions to keep in mind when constructing and interpreting
confidence intervals.
*Our method of calculation assumes that the data come from an SRS of size n from the
population of interest. Other types of random samples (stratified or cluster, say) might be
preferable to an SRS in a given setting, but they require more complex calculations than the
ones we’ll use.
*The margin of error in a confidence interval covers only chance variation due to random
sampling or random assignment. The margin of error is obtained from the sampling
distribution. It indicates how close our estimate is likely to be to the unknown parameter if
we repeat the random sampling or random assignment process many times. Practical
difficulties, such as undercoverage and nonresponse in a sample survey, can lead to
additional errors that may be larger than this chance variation. Remember this unpleasant fact
when reading the results of an opinion poll or other sample survey. The way in which a
survey or experiment is conducted influences the trustworthiness of its results in ways that
are not included in the announced margin of error.